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Observations on Modeling Social Identity: Suggestions to Address the Challenges of Social Identity

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Advances in Social Simulation (ESSA 2022)

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Abstract

In the last two decades, social identity (SI) modeling and simulation have significantly advanced. They are building on and, in many cases, improving the over a half-century of validated SI experimental studies and theories. In this paper, observations on modeling and simulation of SI explore niches of additional opportunities based upon multiple perspectives: the evolution of social organisms, non-competitive theories of evolution, emergent properties of collective problem solving, advances in non-social computational modeling, epidemiological simulations, and complexity science. Based on these observations, specific recommendations are provided for expanding SI modeling and simulation. The main recommendation is to develop a general model of SI based on the observation that all social organisms share common traits, such as the innate drive to form SI or how individual states of uncertainty or stress trigger SI, but also recognize that complex species present more complex expressions of SI. Other recommendations are: SI models must accommodate that not all expressed SI traits have origins in or require higher fitness, all or many SI traits have triggers and maybe trigger thresholds that must be modeled, the inclusion of emergent group performance that may change SI behavior and strategies, and the development of a SI community model for research and realistic applications.

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Correspondence to Norman L. Johnson .

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Johnson, N.L. (2023). Observations on Modeling Social Identity: Suggestions to Address the Challenges of Social Identity. In: Squazzoni, F. (eds) Advances in Social Simulation. ESSA 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-34920-1_23

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